**3. Omics approaches to improve sugarcane crop**

The word "Omics" has become a broader term and it is impossible to cover it just in one topic. Omics approaches have explored understandings of complex interactions between genes, proteins and metabolites. These integrated approaches heavily rely on analytical methods, bioinformatics, computational analysis and many other disciplines of biology. Using genomics, proteomics, transcriptomics and metabolomics approaches, the consistency and predictability in plant breeding and transgenic technology has been improved. It has helped to produce high quality and stress resilient crops with enhanced nutritional value in less time and lower input usage. Omics has provided insights into the molecular mechanisms involved in insect resistance and tolerance to herbicides, cold, salinity and drought stresses [38]. To interpret the omics (genomics, transcriptomics, proteomics and metabolomics) approaches in sugarcane for higher yield, higher sucrose contents, biotic and abiotic stress tolerance, one should have knowledge about the genome structure, physiology and functional veracity of sugarcane with other related crops.

#### **3.1. Genomics**

Sugarcane has a large genome size of 7440 Mb (mega base pairs) having 2n = 100–300. The genome is supposed to be evolved as a result of a complex hybridization event. It is considered that *Saccharum officinarum* is octoploid. The monoploid genome size of *Saccharum officinarum* is 930 Mb and that of *Saccharum spontaneum* is 750 Mb, twice of the size of rice genome (~390 Mb). Geneticists are trying to interpret the associations of complex sugarcane genome with other similar crop plants. The level of genome varies from diploid to decaploid among the *Poaceae* species [39]. The conservation and origin of gene function is suggested by gene order which is maintained by synteny of the genome [40]. The TE (transposable elements) intervening between coding genes strongly support the extension of genome in grasses. Transposons and retrotransposons are two categories of transposable elements. In plants, the most abundant retroelements are LTR (long terminal repeats) retrotransposons. Transposase proteins are involved in insertion-deletion mechanisms. Active sites of the transcription control the movement of retrotransposons, which reinsert them into the genome after each propagation cycle to increase copy number. Recent studies show that there exists a gene remodeling mechanism which results in the generation of new genes. As a result of gene remodeling, gene expression is altered by new regulatory networks [41]. The study of transposable elements in wheat and barley [42] provided close relationship of transposable elements with genome structure. The transposable elements in sugarcane can be activated and evaluated by functional transcriptomic approaches. The major limitation in sugarcane genetic improvement is its genome size. To sequence the genome of an organism Bacterial Artificial Chromosomes (BAC) are used. BAC (Bacterial Artificial Chromosomes) library was constructed with Hin*d*III partial digestion for sugarcane cultivar 'R570' having more than 100,000 clones with 130 mega base pairs (Mb) insert size [43]. For map-based cloning of sugarcane, BAC resources will be highly esteemed and physical map of sorghum (http://www.genome.clemson.edu/tools/contig\_viewer/index. html) will be used as complementary tool.

#### **3.2. Transcriptomics**

Transcriptomic approaches have emerged as an effective tool for functional characterization of unknown genes. In combination with proteomics and metabolomics, these approaches are very useful for the development of improved sugarcane clones. It reduces the complexity of data and targets. Only active genes in the cell or tissues are considered at the time of sampling. By employing transcriptomic approaches, one can easily compare similar type of tissues at different developmental stages in different organisms growing in different conditions [44].

### **3.3. ESTs**

studies to assess the genetic stability of *in vitro* plants. For diploid species, Simple Sequence Repeat (SSR), Amplified Fragment Length Polymorphism (AFLP) and Inter-Simple Sequence Repeat (ISSR) have been used successfully to assess genetic stability of *in-vitro* plants [33]. But, for plants like sugarcane which has complex polyploid genome, these tools are inappropriate as interpretation of the results become tricky [34]. Using microscopic techniques for sugarcane is also difficult because of small size and large number of chromosomes and also due to the presence of various cytotypes [35]. In this context flow cytometry has got attraction as it ensures estimation of relative amounts of plants nuclear DNA quickly and precisely [36]. Cytometry is able to discriminate between plants derived different culture techniques and has extensively been used, in many economically important species such as *Gossypium hirsutum*, *Vitis vinifera*, *Passiflora* spp., *Elaeis guineensis*, *Musa acuminata* and *Prunus cerasus* [37]. Flow cytometric analysis of shoots was performed after every 6 months of storage. As a consequence, a discrete behavior of tested varieties was observed during storage and on average approximately 80% cultures were able to recover. From these findings it is concluded sugarcane genotypes can be maintained in minimal growth condition for extensive periods

The word "Omics" has become a broader term and it is impossible to cover it just in one topic. Omics approaches have explored understandings of complex interactions between genes, proteins and metabolites. These integrated approaches heavily rely on analytical methods, bioinformatics, computational analysis and many other disciplines of biology. Using genomics, proteomics, transcriptomics and metabolomics approaches, the consistency and predictability in plant breeding and transgenic technology has been improved. It has helped to produce high quality and stress resilient crops with enhanced nutritional value in less time and lower input usage. Omics has provided insights into the molecular mechanisms involved in insect resistance and tolerance to herbicides, cold, salinity and drought stresses [38]. To interpret the omics (genomics, transcriptomics, proteomics and metabolomics) approaches in sugarcane for higher yield, higher sucrose contents, biotic and abiotic stress tolerance, one should have knowledge about the genome structure, physiology and functional veracity of

Sugarcane has a large genome size of 7440 Mb (mega base pairs) having 2n = 100–300. The genome is supposed to be evolved as a result of a complex hybridization event. It is considered that *Saccharum officinarum* is octoploid. The monoploid genome size of *Saccharum officinarum* is 930 Mb and that of *Saccharum spontaneum* is 750 Mb, twice of the size of rice genome (~390 Mb). Geneticists are trying to interpret the associations of complex sugarcane genome with other similar crop plants. The level of genome varies from diploid to decaploid among the *Poaceae* species [39]. The conservation and origin of gene function is suggested by gene order which is maintained by synteny of the genome [40]. The TE (transposable elements) intervening

but may lead to genetic variations.

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sugarcane with other related crops.

**3.1. Genomics**

**3. Omics approaches to improve sugarcane crop**

Due to large size and complexity of genome, the whole genome sequence of sugarcane was not available. The genome size of its modern cultivar is considered to be more than 10 GB. From 12th July, 2017, NCBI database has 83,138 GSSs (genome survey sequences), 285,216 ESTs (expressed sequence tags) and 13,382 nucleotide sequences including 491 sequences of mRNA under the search of "*Saccharum*". There are three main groups of ESTs including a large group resulting from a modern variety of sugarcane and two small groups from *Saccharum officinarum* and *Saccharum arundinaceum* (**Table 1**). Majority of ESTs belong to six cultivars from different countries including Australia (Q117), USA (CP72-2086), India (CoS 767, Co 1148) and Brazil (SP80-3280, SP70-1143). Most of the ESTs are from mixed tissue samples of Brazilian varieties i.e. P57150-4 x PB5211 or SP83-5077, RB80-5028; SP80-87432, RB855205, CB47-89, RB845298, SP803280 x SP81-5441, SP80-185, SP80-3280 and SP87-396 [45].

Many projects have been executed for sequencing sugarcane ESTs (expressed sequence tags) in Brazil (http://sucest.lad.ic.unicamp.br/en), South Africa and Australia [46]. Until now more than 0.3 million (300,000) ESTs have been generated. A database holding 0.238 million (238,000) ESTs (constructed from diverse organs and tissues) from 37 libraries was erected by


**3.4. Proteomics**

**3.5. Metabolomics**

Proteomics is the large-scale study of proteome (whole protein contents) and diverse properties of proteins. Through proteomics approaches, we can determine the structural and functional details of biological systems under different conditions. Proteomics has been a major field of functional genomics after the completion of many genome sequencing projects. It has also helped to understand the mode of actions, resistance mechanisms and bio-degradation of pesticides. However, in sugarcane, proteome study is a little bit complicated as no standard protein extraction protocol is available [49]. As compared to other monocots, sugarcane proteomics have not gained momentum yet. Finding protein extraction techniques is a stepping stone in shaping up sugarcane proteomics. Earlier, isoenzyme pattern was used as a tool in sugarcane varietal identification and taxonomy. Isoenzyme pattern was analyzed on 1D gradient polyacrylamide gels based on molecular weight differences [50]. *Saccharum* species (*S. sinense*, *S. edule*, *S. robustum*, *S. spontaneum* and *S. officinarum*) were discriminated from other related genus *Eriochrysis*, *Imperata*, *Narenga*, *Eriochrysis*, *Miscanthus* and *Erianthus* by isozyme pattern of acid phosphatases, leucine aminopeptidases and esterases. O'Farrell [51] introduced 2DE which increased interest in sugarcane proteomics. 2DE technique was then used to study sugarcane roots [52], stalks, leaves [53], meristematic cells and suspension cells [54]. Changes in 2DE protein pattern was observed under different stress conditions. Sample preparation is a crucial step and is necessary for reproducible results. Sugarcane tissues are rigid, fibrous in nature, have sucrose, phenolic compounds and other metabolites in its stalk. Protein extraction protocols have been optimized for the extraction of protein from leaves, meristem and cell suspension cultures but extraction of protein from stalk is still a challenge [55]. Since sugarcane stalk is the core site for sucrose metabolism and host-pathogen interaction but no reproducible protocol is available for the isolation of proteins from stalk tissues.

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Metabolomics is the study of metabolites within the cells, tissues or organism. Proteomics is the study of gene product produced whereas metabolomics explores whether gene products are metabolically active within an organism or not. It also includes role of metabolites in various cellular processes. Hence, metabolites are direct indicators of the performance of a plant under particular biotic or abiotic stresses [56]. Nutritional quality and plant health can be improved by monitoring the changes in metabolite profiling. So, the retrieved informations can effectively be used to develop improved crop varieties as well. Variations in metabolite pattern can also assist to distinguish the mode of action of pesticide which provides critical information for the discovery of new pesticides. Metabolomics may be employed to determine differences and similarities between parents and offsprings on the basis of metabolite composition. Mass spectrometry (MS) and NMR (nuclear magnetic resonance) techniques are used for metabolic profiling, to monitor the metabolic regulations and to analyze the impact of herbicides, pesticides, high temperature, intense light, humidity, soil type, salinity, fertilizers and pests on metabolite composition. One of the fundamental reasons for unavailability of data on sugarcane metabolites is the complexity of sugarcane genome and metabolomes. Most of the research has been focused on differential gene expression. Second constraint is the limited availability of technology due to its sensitivity and labor intensity. Recently,

**Table 1.** ESTs (Expressed sequence tags) and number of nucleotide sequences corresponding to *Saccharum* species and hybrids submitted in db\_EST and db\_Nucleotide, respectively (NCBI: 12th July, 2017).

SUCEST (the Brazilian ONSA consortium's sugarcane EST project). More than 43 thousand clusters (that may signify distinctive transcripts) were assembled by cluster analysis of the SUCEST. A BLAST search showed that almost 50% of these expressed sequence tag clones had no resemblance with known proteins. In Genbank, almost 40% of the clones represent full length protein sequences. The genes involved in diverse metabolic processes have successfully been recognized by analysis of SUCEST database. These analyses reveal that assemblage of ESTs is highly illustrative and indicate tagging of thousands of sugarcane genes [47]. ESTs represent gene encoding sequences, natural antisense transcripts, transacting siRNA precursors, miRNA and most commonly noncoding RNA. The information provided by EST dataset is an important starting point to know about the genome of an organism. It can also help to determine genes of agronomic importance (tolerance of biotic and abiotic stresses, sugar content and mineral nutrition). EST availability makes possible the analyses of gene expression on a large scale. Numerous studies have been conducted for *in-silico* analysis of transcript enrichment using different cDNA libraries [48].

#### **3.4. Proteomics**

Proteomics is the large-scale study of proteome (whole protein contents) and diverse properties of proteins. Through proteomics approaches, we can determine the structural and functional details of biological systems under different conditions. Proteomics has been a major field of functional genomics after the completion of many genome sequencing projects. It has also helped to understand the mode of actions, resistance mechanisms and bio-degradation of pesticides. However, in sugarcane, proteome study is a little bit complicated as no standard protein extraction protocol is available [49]. As compared to other monocots, sugarcane proteomics have not gained momentum yet. Finding protein extraction techniques is a stepping stone in shaping up sugarcane proteomics. Earlier, isoenzyme pattern was used as a tool in sugarcane varietal identification and taxonomy. Isoenzyme pattern was analyzed on 1D gradient polyacrylamide gels based on molecular weight differences [50]. *Saccharum* species (*S. sinense*, *S. edule*, *S. robustum*, *S. spontaneum* and *S. officinarum*) were discriminated from other related genus *Eriochrysis*, *Imperata*, *Narenga*, *Eriochrysis*, *Miscanthus* and *Erianthus* by isozyme pattern of acid phosphatases, leucine aminopeptidases and esterases. O'Farrell [51] introduced 2DE which increased interest in sugarcane proteomics. 2DE technique was then used to study sugarcane roots [52], stalks, leaves [53], meristematic cells and suspension cells [54]. Changes in 2DE protein pattern was observed under different stress conditions. Sample preparation is a crucial step and is necessary for reproducible results. Sugarcane tissues are rigid, fibrous in nature, have sucrose, phenolic compounds and other metabolites in its stalk. Protein extraction protocols have been optimized for the extraction of protein from leaves, meristem and cell suspension cultures but extraction of protein from stalk is still a challenge [55]. Since sugarcane stalk is the core site for sucrose metabolism and host-pathogen interaction but no reproducible protocol is available for the isolation of proteins from stalk tissues.

#### **3.5. Metabolomics**

SUCEST (the Brazilian ONSA consortium's sugarcane EST project). More than 43 thousand clusters (that may signify distinctive transcripts) were assembled by cluster analysis of the SUCEST. A BLAST search showed that almost 50% of these expressed sequence tag clones had no resemblance with known proteins. In Genbank, almost 40% of the clones represent full length protein sequences. The genes involved in diverse metabolic processes have successfully been recognized by analysis of SUCEST database. These analyses reveal that assemblage of ESTs is highly illustrative and indicate tagging of thousands of sugarcane genes [47]. ESTs represent gene encoding sequences, natural antisense transcripts, transacting siRNA precursors, miRNA and most commonly noncoding RNA. The information provided by EST dataset is an important starting point to know about the genome of an organism. It can also help to determine genes of agronomic importance (tolerance of biotic and abiotic stresses, sugar content and mineral nutrition). EST availability makes possible the analyses of gene expression on a large scale. Numerous studies have been conducted for *in-silico* analysis of transcript

**Table 1.** ESTs (Expressed sequence tags) and number of nucleotide sequences corresponding to *Saccharum* species and

hybrids submitted in db\_EST and db\_Nucleotide, respectively (NCBI: 12th July, 2017).

*Saccharum* **species and hybrids No. of ESTs Nucleotide sequences**

*Saccharum officinarum* 20,701 7066 *Saccharum arundinaceum* 341 234 *Saccharum* hybrid cultivar 284,482 2267 Mixed cultivar of *Saccharum* hybrid 73,778 10 SP80-3280 cultivar of *Saccharum* hybrid 135,534 54 CoS 767 cultivar of *Saccharum* hybrid 25,382 – Q117 cultivar of *Saccharum* hybrid 9141 54 SP70-1143 cultivar of *Saccharum* hybrid 24,313 8 Co 1148 cultivar of *Saccharum* hybrid 1069 2 CP72-2086 cultivar of *Saccharum* hybrid 7993 4 Co 740 cultivar of *Saccharum* hybrid 310 25 CoC 671 cultivar of *Saccharum* hybrid 315 67 NCo376 cultivar of *Saccharum* hybrid 535 11 H50-7209 cultivar of *Saccharum* hybrid 27 3 F134 cultivar of *Saccharum* hybrid 4 – Co 62175 cultivar of *Saccharum* hybrid 206 1 Co 86032 cultivar of *Saccharum* hybrid 30 101 Unknown cultivar of *Saccharum* hybrid 3904 339 **Total** 285,216 13,382

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enrichment using different cDNA libraries [48].

Metabolomics is the study of metabolites within the cells, tissues or organism. Proteomics is the study of gene product produced whereas metabolomics explores whether gene products are metabolically active within an organism or not. It also includes role of metabolites in various cellular processes. Hence, metabolites are direct indicators of the performance of a plant under particular biotic or abiotic stresses [56]. Nutritional quality and plant health can be improved by monitoring the changes in metabolite profiling. So, the retrieved informations can effectively be used to develop improved crop varieties as well. Variations in metabolite pattern can also assist to distinguish the mode of action of pesticide which provides critical information for the discovery of new pesticides. Metabolomics may be employed to determine differences and similarities between parents and offsprings on the basis of metabolite composition. Mass spectrometry (MS) and NMR (nuclear magnetic resonance) techniques are used for metabolic profiling, to monitor the metabolic regulations and to analyze the impact of herbicides, pesticides, high temperature, intense light, humidity, soil type, salinity, fertilizers and pests on metabolite composition. One of the fundamental reasons for unavailability of data on sugarcane metabolites is the complexity of sugarcane genome and metabolomes. Most of the research has been focused on differential gene expression. Second constraint is the limited availability of technology due to its sensitivity and labor intensity. Recently, metabolome (whole metabolites in a specific tissue) has been used as a tool for understanding metabolic regulations. This work was accomplished by some advanced technologies where multiple metabolites were determined in a particular tissue within an hour simultaneously. GCMS (gas chromatography-mass spectrometry) is a vastly used technique that separates the metabolites of different types and identify them on the basis of mass spectral matching and retention time. Identification and extraction methods were optimized for thirty sugarcane metabolites. Hence, metabolome studies are of pivotal importance to understand interaction between the genes and their resultant proteins which can be used to understand mechanisms of sucrose accumulation in sugarcane [57].

method [62]. The selected transformants were resistant against commercial formulation of ammonium glufosinate. Southern blot analysis was used to confirm the stable integration of *neo* and *PAT* genes. While western blot analysis and RT-qPCR were used to analyze the expression of these genes. Another report was given by Manickavasagam et al. [5]. They developed herbicide tolerant sugarcane plants by *Agrobacterium* mediated transformation. This was first report of *Agrobacterium* mediated transformation in which axillary buds from 6 months old plants were used as explant. *Agrobacterium* with binary vector pGA492 having *β-glucuronidase*, *neomycin phosphotransferase II* and *bar* genes in between the T-DNA regions was used for transformation. This study proved that phosphinothricin (5.0 mg/L) is more effective selective agent as compared with kanamycin and geneticin. Southern blot analysis was used to confirm the transformants. Leibbrandt and Snyman [7] reported the transformation of *pat* gene in NCo 310 genotype of sugarcane which confers resistance to the herbicide

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Basta. Stable transgene expression was evaluated in glasshouse and field conditions.

Insect pests are one of the major yield limiting agents which cause serious losses to crop yield. Economically important insect pests of sugarcane can be categorized into borers, sap sucking pests, white grubs and termites. Sugarcane pests show extensive variation in species composition in different tropic and subtropic agro climatic regions. All around the world, sugarcane is facing problems of insect pests and diseases which are seriously affecting sugar production. No exact estimates are available for these cumulative losses caused by the insect pests and diseases. Anyhow, economic losses caused by certain pests has been estimated. Annual loss of \$10–\$20 million were estimated to sugar industry at Lower Rio Grande Valley of Texas only by *E. loftini*. Similarly, wooly aphid (*Ceratovacuna lanigera*) has been estimated to cause 18.3% yield losses during sixth months [63]. Most of the sugarcane cultivars growing in the field are outcomes of hybridization and selection. Advancements in molecular biology and genetic transformation have helped researchers to develop transgenic sugarcane plants with desired agronomic traits particularly for insect pest resistance. Different types of molecules have been manipulated to produce insect resistant plants such as lectins, proteinase inhibitors, ribosome inactivating proteins, secondary metabolites, delta endotoxins and insecticidal proteins.

Considerable advancements have been made to develop transgenic sugarcane having resistance against lepidopteran borers such as *E. loftini*, *D. saccharalis*, *S. excerptalis* and *C. infuscatellus* by introducing various cry genes. *Bacillus thuringiensis* derived cry genes encoding toxins have been expressed in sugarcane to engineer resistance against insect pests. First transgenic sugarcane was developed by Arencibia [6] against *D. saccharalis*. Five transformation events were selected exhibiting considerable resistance against borer in spite of very low expression (0.59–1.35 ng/mg of soluble leaf protein) of transgene. Truncated *cry1A(b)* gene was expressed in sugarcane under *CaMV 35S* promoter. Lower level of expression was observed in transgenic plants perhaps because of lower activity of the aforementioned promoter in monocots. Low to medium level internode invasions were also observed in the transgenic lines. Transgenic lines were developed with modified GC contents (37.4–47.5%) of *cry1Ac* gene and effect of change in GC contents, was

*4.1.2. Insect resistance*
